Medicine and Health Sciences Commons^™

The Contribution Of Micrornas To Rybp Silencing In Glioblastoma Multiforme, Alex B. Lee

Graduate Theses

Glioblastoma multiforme (GBM) is a highly aggressive and invasive tumor of the central nervous system (CNS). Survival rates are abysmal, with only 7.2% of patients alive 5-years after diagnosis. Because of this, understanding epigenetic alterations that give GBM tumors their aggressive phenotypes is critical for the development of more targeted and effective therapies. These alterations frequently affect a group of proteins called the Polycomb group proteins, which play important oncogenic and tumor suppressive roles in cancer. One Polycomb protein, the RING1- and YY1-binding protein (RYBP), is downregulated in a majority of GBM patients, suggesting a strong tumor suppressive property. In …

Bcl2 Mediated Targeted Drug Delivery For The Treatment Of Kidney Fibrosis And Stomach Cancer, Humayra Afrin

Open Access Theses & Dissertations

Apoptosis, the programmed death of cells, is primarily regulated by a delicate balance between pro-apoptotic and anti-apoptotic signals. The Bcl-2 (B-cell lymphoma 2) family of proteins acts as anti-apoptotic agents, promoting cell survival. Dysregulation of these proteins is a common occurrence in conditions such as cancer and fibrosis, where overexpression of anti-apoptotic members can foster tumor cell survival and fibroblast activation. In this study, our aim was to explore the therapeutic potential of Bcl-2 inhibitors, both as a small molecule (specifically Navitoclax (Navi)), inhibitor and as Bcl-2 siRNA, for targeted treatment. Intravenous administration of Navi often leads to thrombocytopenia, necessitating …

Selective Transfection Of A Transferrin Receptor-Expressing Cell Line With Dna-Lipid Nanoparticles And Synthesis Of Parasite-Derived Glycans As Biomarkers For Leishmaniasis, Irodiel Vinales Lozano

Open Access Theses & Dissertations

Despite notable progress in lipid nanoparticle (LNP)-mediated gene delivery, achieving selective transfection of specific cell types, such as cancer cells, remains a significant hurdle, hindering the advancement of innovative gene therapies. In this study, we engineered an LNP formulation encapsulating plasmid DNA (pDNA) encoding the monomeric Green Lantern (mGL) fluorescent reporter protein. The DT7 peptide ligand targeting human transferrin receptor 1 (hTfR1) was also conjugated to the LNP surface for targeted delivery to hTfR1-expressing cells. Optimization of LNP composition yielded favorable particle diameter, ζ-potential, yield, and pDNA encapsulation efficiency. Evaluation of transfection selectivity using a panel of two engineered cell …

Advancing Brain Tumor Segmentation With Spectral–Spatial Graph Neural Networks, Sina Mohammadi, Mohamed Allali

Engineering Faculty Articles and Research

In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …

Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast

Honors Theses and Capstones

Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

Using Machine Learning To Assist Auditory Processing Evaluation, Hasitha Wimalarathna, Sangamanatha Veeranna, Minh Vu Duong, Chris Allan Prof, Sumit K. Agrawal, Prudence Allen, Jagath Samarabandu, Hanif M. Ladak

Electrical and Computer Engineering Publications

Introduction: Approximately 0.2–5% of school-age children complain of listening difficulties in the absence of hearing loss. These children are often referred to an audiologist for an auditory processing disorder (APD) assessment. Adequate experience and training is necessary to arrive at an accurate diagnosis due to the heterogeneity of the disorder.

Objectives: The main goal of the study was to determine if machine learning (ML) can be used to analyze data from the APD clinical test battery to accurately categorize children with suspected APD into clinical sub-groups, similar to expert labels.

Methods: The study retrospectively collected data from 134 children referred …

Dense & Attention Convolutional Neural Networks For Toe Walking Recognition, Junde Chen, Rahul Soangra, Marybeth Grant-Beuttler, Y. A. Nanehkaran, Yuxin Wen

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait disorder where children’s initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are …

Development Of A Cost-Constrained Intelligent Prosthetic Knee With Real-Time Machine Learning, Predictive Stumble Control, Lucas Jonathan Galey

Open Access Theses & Dissertations

The field of biomechatronics is evolving quickly with advances in computer science, biology, and electrical and mechanical engineering. Coupled with increased interests in machine learning (ML) across all industry sectors, there are opportunities to leverage advanced analytics in uniquely complex problems. This study aimed to deploy real-time ML predictions in a novel microprocessor-controlled prosthetic knee (MPK) device capable of identifying and responding to stumble-events to reduce amputee fall prevalence. Innately, stumbling is a chaotic event. Current MPKs operate by detecting gait characteristics and reacting to preprogrammed states. While these systems are beneficial in significant ways, such as energy expenditure and …

Split And Join: An Efficient Approach For Simulating Stapled Intestinal Anastomosis In Virtual Reality, Di Qi, Suvranu De

Engineering Faculty Articles and Research

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using …

An Xai Approach For Covid-19 Detection Using Transfer Learning With X-Ray Images, Salih Sarp, Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali, Huseyin Kusetogullari, Yanxiao Zhao, Gungor Ates, Ozgur Guler

Engineering Technology Faculty Publications

The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Virtual Surgical Planning In Craniomaxillofacial Surgery: A Structured Review, Kaye Verlarde, Rentor Cafino, Armando Isla Jr., Karen Mae Ty, Xavier-Lewis Palmer, Lucas Potter, Larry Nadorra, Luchin Valrian Pueblos, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Craniomaxillofacial (CMF) surgery is a challenging and very demanding field that involves the treatment of congenital and acquired conditions of the face and head. Due to the complexity of the head and facial region, various tools and techniques were developed and utilized to aid surgical procedures and optimize results. Virtual Surgical Planning (VSP) has revolutionized the way craniomaxillofacial surgeries are planned and executed. It uses 3D imaging computer software to visualize and simulate a surgical procedure. Numerous studies were published on the usage of VSP in craniomaxillofacial surgery. However, the researchers found inconsistency in the previous literature which prompted the …

Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Exploits of technology have been an issue in healthcare for many years. Many hospital systems have a problem with “disruptive innovation” when introducing new technology. Disruptive innovation is “an innovation that creates a new market by applying a different set of values, which ultimately overtakes an existing market” (Sensmeier, 2012). Modern healthcare systems are historically slow to accept new technological advancements. This may be because patient-based, provider-based, or industry-wide decisions are tough to implement, giving way to dire consequences. One potential consequence is that healthcare providers may not be able to provide the best possible care to patients. For example, …

Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk

School of Cybersecurity Faculty Publications

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the …

Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …

Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette

Electrical & Computer Engineering Faculty Publications

This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The …

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …

Identifying And Minimizing Underspecification In Breast Cancer Subtyping, Jonathan Cheuk-Kiu Tang

Master's Theses

In the realm of biomedical technology, both accuracy and consistency are crucial to the development and deployment of these tools. While accuracy is easy to measure, consistency metrics are not so simple to measure, especially in the scope of biomedicine where prediction consistency can be difficult to achieve. Typically, biomedical datasets contain a significantly larger amount of features compared to the amount of samples, which goes against ordinary data mining practices. As a result, predictive models may fail to find valid pathways for prediction during training on such datasets. This concept is known as underspecification.

Underspecification has been more accepted …

Frontiers In The Self-Assembly Of Charged Macromolecules, Khatcher O. Margossian

Doctoral Dissertations

The self-assembly of charged macromolecules forms the basis of all life on earth. From the synthesis and replication of nucleic acids, to the association of DNA to chromatin, to the targeting of RNA to various cellular compartments, to the astonishingly consistent folding of proteins, all life depends on the physics of the organization and dynamics of charged polymers. In this dissertation, I address several of the newest challenges in the assembly of these types of materials. First, I describe the exciting new physics of the complexation between polyzwitterions and polyelectrolytes. These materials open new questions and possibilities within the context …

The Development Of A Motion Sensing Device For Use In A Home Setting, Jaspreet K. Kalsi

Electronic Thesis and Dissertation Repository

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, with over 10 million individuals diagnosed with PD world-wide. The most common symptom characterized by PD is tremor. Tremor is an involuntary oscillatory motion that most prominently occurs in upper limb, specifically in the hand and wrist that has a measurable frequency and amplitude. This thesis aims to evaluate the usability and functionality of a tremor sensing device designed to collect quantitative data on individuals with PD. The designed device uses 23 commercially-available inertial measuring units (IMUs) located between 21 joints: distal interphalangeal (DIP) joints, proximal interphalangeal (PIP) joints, Interphalangeal …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Hsp90 Inhibitors Modulate Sars-Cov-2 Spike Protein Subunit 1-Induced Human Pulmonary Microvascular Endothelial Activation And Barrier Dysfunction, Ruben Manuel Luciano Colunga Biancatelli, Pavel Solopov, Betsy W. Gregory, Yara Khodour, John D. Catravas

Bioelectrics Publications

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused more than 5 million deaths worldwide. Multiple reports indicate that the endothelium is involved during SARS-Cov-2-related disease (COVID-19). Indeed, COVID-19 patients display increased thrombophilia with arterial and venous embolism and lung microcapillary thrombotic disease as major determinants of deaths. The pathophysiology of endothelial dysfunction in COVID-19 is not completely understood. We have investigated the role of subunit 1 of the SARS-CoV-2 spike protein (S1SP) in eliciting endothelial barrier dysfunction, characterized dose and time relationships, and tested the hypothesis that heat shock protein 90 (HSP90) inhibitors would prevent and repair such injury. S1SP …

Speaker Encoding For Zero-Shot Speech Synthesis, Tristin W. Cory

MSU Graduate Theses

Spoken communication, for many, is an essential part of everyday life. Some individuals can lose or not be born with the ability to speak. To function on a day-to-day basis, these individuals find other ways of communication. Adaptive speech synthesis is one of those ways. It recreates a user’s previous voice or creates a voice that blends with their regional dialect. Current adaptive speech synthesis techniques that achieve human-like speech require thirty minutes, to a few hours of high-quality audio recordings of a target speaker. This amount of recorded audio is not commonly possessed by people in need of a …

Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can …